AIMC Topic: Meningeal Neoplasms
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An MRI-based deep transfer learning radiomics nomogram for predicting meningioma grade.
Scientific reports
May 13, 2025
The aim of this study was to establish a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features to predict meningioma grade. Three hundred forty meningiomas from one hospital composed the training set, and 102 meningiomas fr...
Machine Learning Analysis of Single-Voxel Proton MR Spectroscopy for Differentiating Solitary Fibrous Tumors and Meningiomas.
NMR in biomedicine
May 1, 2025
Solitary fibrous tumor (SFT), formerly known as hemangiopericytoma, is an uncommon brain tumor often confused with meningioma on MRI. Unlike meningiomas, SFTs exhibit a myoinositol peak on magnetic resonance spectroscopy (MRS). This study aimed to de...
Stiffness analysis of meningiomas using neural network-based inversion on MR Elastography.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Meningiomas are the most prevalent benign intracranial tumors, and surgical intervention is the primary treatment. The physical characteristics of meningiomas, such as tumor stiffness or consistency, play a crucial role in the surgical approach. This...
Predicting Discharge Disposition Following Meningioma Resection Using a Multi-Institutional Natural Language Processing Model.
Neurosurgery
Mar 15, 2021
BACKGROUND: Machine learning (ML)-based predictive models are increasingly common in neurosurgery, but typically require large databases of discrete variables for training. Natural language processing (NLP) can extract meaningful data from unstructur...
Training Deep Neural Networks for Small and Highly Heterogeneous MRI Datasets for Cancer Grading.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2020
Using medical images recorded in clinical practice has the potential to be a game-changer in the application of machine learning for medical decision support. Thousands of medical images are produced in daily clinical activity. The diagnosis of medic...
A Deep Convolutional Neural Network With Performance Comparable to Radiologists for Differentiating Between Spinal Schwannoma and Meningioma.
Spine
May 15, 2020
STUDY DESIGN: Retrospective analysis of magnetic resonance imaging (MRI).
Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks.
Journal of digital imaging
Aug 1, 2019
Deep learning with convolutional neural networks (CNNs) has experienced tremendous growth in multiple healthcare applications and has been shown to have high accuracy in semantic segmentation of medical (e.g., radiology and pathology) images. However...
Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging.
Neurosurgical focus
Nov 1, 2018
OBJECTIVEPrognostication and surgical planning for WHO grade I versus grade II meningioma requires thoughtful decision-making based on radiographic evidence, among other factors. Although conventional statistical models such as logistic regression ar...
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